Incentive-based Demand Response Research Articles

A multi-objective energy scheduling of the reconfigurable off-grid microgrid with electric vehicles using demand response program

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations’ sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong’s (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively.

Social welfare maximization with efficient energy management of community microgrid considering customer behavioral response using MDCLPIS

A novel residential demand response (DR) program is proposed to encourage microgrid customers to participate in demand reduction using a house energy consumption scheduler (HECS) to maximize social welfare by optimally reducing the cost of electricity and to receive maximum incentives. In this research, both Price-based and incentive-based DR programs are considered, and the demand-price elasticity approach is implemented to model them. To make the DR model more effective, customer behavioral response is taken into account, by leveling the customers according to their price elasticity. An optimized non-cooperative energy game theory framework and a Nash equilibrium strategy are employed to model customer response under a multi-time of use (TOU) pricing and incentive scheme, with the aim of achieving the best scheduling of home-controlled appliances while preserving customers’ privacy. This approach maximizes social welfare by minimizing the electricity consumption cost for customers while simultaneously saving on generation costs. The negative externality effect on price rate is internalized between the customers of different consumption levels using multi-dynamic consumption-level pricing and incentive scheme (MDCLPIS). The suggested model distinguishes between consumers’ behavior in response to changes in energy prices and their behavior in response to changes in incentives. Finally, numerical and simulation results demonstrate the suitability and effectiveness of the proposed model and its advantages in terms of customer saved cost by up to 21.26%, microgrid saved cost by up to 29.44%, and getting benefits by optimal scheduling of their load.

Design and implementation of smart home energy management system for Indian residential sector

Residential power consumption has surged with the widespread adoption of smart appliances, prompts utilities to implement Demand Response(DR) programs. Home Energy Management Systems (HEMS) plays a vital role in these initiatives within the smart grid infrastructure. The rapid advancement of wireless communication technologies, especially the Internet of Things (IoT), will enable successful architectural designs for HEMS. This study proposes an incentive-based DR implementation using IoT-enabled HEMS for the Indian residential sector that represents 25% of the total power demand. The proposed HEMS automates the management of categorized loads during peak hours through load shifting and scheduling. The proposed algorithm is deployed in real-time residential setup using IoT modules. Emphasizing sustainability and consumer affordability, the work integrates a Photo-Voltaic (PV) based Battery Storage System (BSS) to support critical loads during peak hours. Consumer load preferences obtained through a survey are incorporated into the deployment of the proposed HEMS. Experimental studies demonstrated significant energy savings of 49% with the loads under consideration and substantial cost savings like approximately 49% with Load Management (LM) alone, 53% with the inclusion of Time of Day (ToD) tariff, and an impressive 78% with the proposed incentives for participating in DR. Additionally, this study highlights the synergistic savings realized through the combined implementation of the proposed LM strategy and ToD tariff in India.

A PEM-based augmented IBDR framework and its evaluation in contemporary distribution systems

Demand response (DR) is an attractive concept that invites customers’ active participation in the distribution sector by means of price elasticity of demand (PED). It not only enhances customers’ demand sensitivity but also improves technicalities and economics related to both the utility and demand sides. This paper emphasizes the combined effect of price-based DR (PBDR) and incentive-based DR (IBDR) with the inclusion of PED. The elasticity phenomenon, when applied with incentives as in IBDR, changes the demand-consumption pattern as compared to individual DR. Moreover, the demand variation due to only incentives leads to incentive elasticity, which needs to be studied carefully; then only the impact of individual DR and augmented DR (PBDR and IBDR combined) can be understood analytically. In this work, IBDR models are tested on considered pricing schemes along with a new proposed pricing scheme to evaluate the systems’ technical and economical parameters. A standard IEEE 33 bus distribution system has been chosen for the assessment of suggested models and to compare them to the existing ones. Furthermore, these models are descriptively evaluated from both the utility and consumer perspectives.

Optimal allocation of customer energy storage based on power big data and improved LSTM load forecasting

This research explores the potential of energy storage investment with a focus on regional power users. An incentive-based demand response framework is constructed, emphasizing the aggregation of demand-side resources for active participation in the power market. Utilizing a data-driven approach, the improved Long Short Term Memory (LSTM) model is employed to predict customer behavior in response to incentives. The primary objective is to maximize the life cycle benefit while minimizing the payback period for users investing in energy storage. By harnessing big data analytics, suitable users for energy storage investment are identified and optimal capacity allocation is determined. Given the current energy storage parameters and dynamics of electricity pricing, boundary values for profitable user energy storage capacities are derived. This methodology filters users with capacities exceeding these thresholds, marking them as potential candidates for energy storage deployment. Furthermore, a measurement model for iron phosphate batteries is introduced. Analysis of adjustable resource capacity, duration, and benefits for potential users provides insights into optimal energy storage investment strategies. Integrating configured energy storage batteries with time-of-use tariffs substantially reduces energy storage costs, leading to enhanced economic efficiency.

Optimal Power Dispatch for a Chlorine Factory With Fuel Cells Participating in Incentive-Based Demand Response

Optimal Power Dispatch for a Chlorine Factory With Fuel Cells Participating in Incentive-Based Demand Response

Optimal scheduling of the CSP–PV–wind hybrid power generation system considering demand response

In order to solve the problem of there being a high proportion of wind and photovoltaic (PV) abandonment in the new energy system, an optimal dispatching method of concentrated solar power (CSP)–PV–wind hybrid power generation considering demand response is proposed. First, on the basis of the combination of wind and PV power stations and CSP stations, electric heating (EH) devices are used to convert unconsumed wind and PV power into thermal energy stored in the thermal storage system of the CSP station. Second, based on the difference in response time and response volume of customers, a stepped incentive-based demand response (IBDR) approach is introduced. Thus, a CSP–PV–wind power optimal dispatch model taking into account IBDR is established to achieve coordinated source-load dispatch. Finally, the CPLEX solver is invoked to optimize day-ahead scheduling with the goal of the lowest system comprehensive cost. Example analysis is used to confirm the proposed method’s usefulness in enhancing the system’s wind and PV power consumption capacity and reducing the system’s comprehensive cost.

Droop parameter-based economic dispatch for islanded microgrid considering demand response

The droop parameter setting of the distributed generator (DG) has an essential role in islanded microgrid (MG) systems. This paper presents droop-based economic load dispatch (ELD) in an islanded MG. In this approach, the Power–frequency (P–f) droop characteristic of DG is integrated into its incremental cost, which is the derivative of the cost function of DG. The optimisation problem is formulated by considering the standard deviations of the incremental costs of DGs. Under steady-state conditions, DGs operate at a single frequency and the P–f droop-based ELD ensures that the incremental costs of DGs are equal, minimising the overall cost of active power generation, following the equal incremental cost principle. Furthermore, an incentive-based demand response (DR) programme is integrated into the system to maximise the utility benefit function by incentivising customers for power curtailment. The results exhibit significant power curtailment during peak load hours, indicating the effectiveness of the DR programme. The proposed combined mathematical model is applied to the modified 33-bus islanded MG and solved using basic particle swarm optimisation. It calculates optimal droop parameters for a day-ahead scenario and emphasises the efficiency benefits of optimised droop settings and the integration of DR in islanded MGs.

Incentive-based demand response optimization method based on federated learning with a focus on user privacy protection

Considering the flexible capacity and privacy needs of numerous flexible energy users in the context of demand response (DR), this study establishes an influence model (IM) to describe the DR participation capabilities of users considering privacy budget. Using the designed Stackelberg game mechanism that can achieve optimal selection for DR responders from users with different characteristics described by IM, a federated learning (FL)-based optimization method that uses differential privacy (DP) as the data transmission mechanism for DR economic optimal dispatch is proposed. Ideas of controlling the number of participating users and financially compensating for the privacy leakage risk by the FL-based optimization method are the guarantees for users with private data to participate in DR. The performance of the proposed optimization method is also compared with that of the Moth-flame optimization algorithm in a case study, and the guiding value of the former in selecting among user groups with different characteristics is then discussed. Results show that the proposed method exhibits good economic benefits and universal applicability.

Integration of mobile power-hydrogen storage systems in distribution-level networks: A fuzzy information gap optimization framework

In recent years, renewable energy sources (RESs) have had unquestionably significant benefits. However, maximum penetration of RESs has always been challenging due to their intermittent nature. To address this challenge, this study advances a framework for the flexibility of the distribution network (DN) by employing transportable electrical storages (TESs) and transportable hydrogen storages (THSs). To do so, the TESs are considered to transfer electrical energy among high-penetrated and high-demand points. In addition, curtailed renewable power can be converted to green hydrogen and transferred to hydrogen refueling stations (HRSs) using the THSs. To further flexibility, this work considers an incentive-based demand response (DR) program and vehicle-to-parking lot (V2PL) capable plug-in electric vehicles (PEVs). Moreover, a binary-based method is developed to carry out the trajectory planning of the TESs and THSs and the Floyd algorithm is exploited to minimize traveling and simulation time. Furthermore, the fuzzy information gap decision theory (IGDT) is proposed to enhance the scheduling robustness against the uncertainty of RESs. The obtained results corroborated the positive impact of the TESs and THSs by declining the operation cost by about 4.8 %. Moreover, the proposed uncertainty management model is quite robust against RESs’ uncertainty by achieving about 37.5 % less operation cost.

A Multilevel Model of Energy Market Considering Coupon Incentive Based Demand Response with Wind Power Uncertainty

Demand response (DR) can decrease or increase energy consumption and has the ability to use generating assets more efficiently. Because of the increased power consumption, it has become necessary for the power system networks either to update or upgrade the already existing power system or to inculcate some schemes to minimize the load through effective customer engagement. To address this issue incentive-based demand response proved to be an effective tool to change the pattern of consumption. This work proposes a novel multilevel energy market considering coupon incentive based DR (C-IBDR). The proposed model is operated on a modified PJM 5-Bus system where the independent service operator (ISO) using economic dispatch (ED) computes the localized marginal pricing (LMP) and the load serving entity (LSE) issues coupons to consumers and procures the demand reduction from them. Two wind generators are incorporated into the test system and their effect on the localized marginal pricing with and without demand response is analyzed. The optimization problems are modeled using particle swarm optimization (PSO) and linear programming (LP) in MATLAB. The results demonstrate that the proposed C-IBDR reduces peak demand from 900 to 860 MW through the utilization of wind and DR. This alteration modifies the load curve, increases the power factor from 0.524 to 0.555, raises the revenue of LSEs from $106,646 to $106,946, reduces generation costs from $473,291 to $214,651, and procures a total of 262 MW of demand from the consumer side in the form of demand reduction. The system is further tested for scalability. The practicality of the suggested approach in handling more extensive systems is vividly demonstrated through its application to the modified IEEE 118-bus system.

A multistage coupon incentive-based demand response in energy market

Demand Response (DR) has the ability to use generating assets more efficiently. Because of the increased power consumption, it has become necessary for the power system networks either to upgrade the already existing power system or to inculcate some schemes to minimize the load. To address this issue incentive-based demand response proved to be an effective tool to change the pattern of consumption. This work proposes a novel three-tiered hierarchical CIB-DRP operated on a modified PJM 5-Bus system, where ISO’s economic dispatch computes the localized marginal pricing, LSE issues coupons and consumers provide the demand reduction. Two wind generators are incorporated to the test system and its effect on the system is analyzed. The optimization problems are modelled using PSO and LP in MATLAB. The results demonstrated that the proposed CIB-DRP reduces the peak demand, flattens the load curve, and increases the power factor.

Enhancing Peak Shaving through Nonlinear Incentive-Based Demand Response: A Consumer-Centric Utility Optimization Approach

This paper presents a comparative study on the implementation of incentive-based demand response programs in power grid management, comparing the results obtained from linear and nonlinear models. The challenges faced by power grids in balancing supply and demand and managing price spikes during peak periods are addressed, and demand response programs are proposed as an effective strategy. The focus is on implementing incentive-based programs where load serving entities determine the optimal incentive price and demand reduction. A novel approach is presented for simulating consumer behavior based on the price elasticity of demand and consumers’ utility function, incorporating both linear and nonlinear economic models. The calculation of demand reduction aims to maximize consumers’ welfare, while the determination of the optimal incentive price maximizes the profit of the load serving entities. Real data are utilized, and the proposed models are implemented using the mixed-integer nonlinear programming (MINLP) method. The results demonstrate the effectiveness of providing incentives to consumers during peak hours compared to other approaches, with a comparison between the linear and nonlinear models.

Incentive-based demand response under incomplete information based on the deep deterministic policy gradient

Incentive-based demand response (IBDR), as an important measure to encourage the users to participate in the demand-side management, is commonly modeled as the Stackelberg game with the complete information. However, it is difficult to acquire the users' complete information due to privacy protections. In this paper, a Markov decision process (MDP) game model is proposed to address IBDR under the incomplete information, which is based on a deep deterministic policy gradient algorithm (DDPG). Considering differences on the users' load, the K-means method is used to classify different users according to the daily load rate and the peak-to-valley difference, such that different types of user load will have different incentive prices to participate in demand responses. The proposed DDPG algorithm can improve the calculation efficiency of the Nash equilibrium solution of the IBDR under the incomplete information, as it can deal with the multi-dimensional continuous state and action spaces. Simulation results show that the proposed approach can achieve the Nash equilibrium under the incomplete information and has the higher calculation accuracy and the lower calculation time in comparison to the Q learning method.

A Bi-Level Decision Framework for Incentive-Based Demand Response in Distribution Systems

In a growing retail electricity market, demand response (DR) is becoming an integral part of the system to enhance economic and operational performances. This is rendered as incentive-based DR (IBDR) in the proposed study. It presents a bi-level decision framework under the ambit of multiple demand response providers (DRPs) in the retail competition. It is formulated as a multi-leader-multi-follower game, where multiple DRPs, as the DR stakeholders, are strategically interacting to optimize load serving entity cost at the upper level, and individual DRP as the aggregated customers is optimizing its cost at the lower level. The strategic behavior of DRPs is modeled in a game-theoretic framework using a generalized Stackelberg game. Further, the existence and uniqueness of the game are validated using variational inequalities. It is presented as a nonlinear problem to consider AC network constraints. An equilibrium problem with equilibrium constraints is used as a mathematical program to model the multi-leader-multi-follower, bi-level problem, which is simultaneously solved for all DRPs. The diagonalization method is employed to solve the problem. The detailed numerical analyses are conducted on IEEE 33-bus test and Indian-108 bus distribution systems to demonstrate the applicability and scalability of the proposed model and the suggested method.

Real-Time Incentive Design Under Unknown Demand Response Curves via a Proportional–Integral Control Frameworks

Implementing voluntary Demand Response (DR) in real-time or close to real-time is technically feasible because of the deployment of smart grid infrastructures such as smart meters, remote control units, and mobile communication devices. However, most cases require estimating/predicting load demand curves and typically operate from an electricity market clearing perspective. In this work, two frameworks of voluntary incentive-based DR to enhance the resilience and reliability of the system are designed, including a single Proportional-Integral controller based real-time DR framework and a multiple PI controllers cooperation based DR framework, which can be operated in real-time without the requirement of estimating or predicting the load demand curves. Under the proposed frameworks, the overall system can converge to the optimal tracking point to improve resilience/reliability via the cooperation of the different DR devices. Additionally, the corresponding incentive payments can be precisely determined. In particular, both frameworks are proven to be stable. In addition, deep reinforcement learning is adopted to enhance the performance of the proposed frameworks. Because of the generality of the proposed frameworks and the simplicity of the PI controllers, our proposed DR frameworks are promising for practical implementation. Finally, numerical studies verify the effectiveness of the proposed frameworks.

Urban virtual power plant operation optimization with incentive-based demand response

Urban virtual power plant (VPP) shows great potential in alleviating urban power shortage or power supply-demand imbalance. This study proposes a bi-layer optimization model considering incentive-based demand response (IDR) to optimize the operation scheduling of urban VPP. The bi-layer optimization model includes the lower-layer IDR model and the upper-layer urban VPP optimal operation scheduling model. In the lower-layer IDR model, user satisfaction, comfort and preference are considered. Users mainly participate in the scheduling of urban VPP by IDR. The demand response resources obtained by urban VPP through providing incentives for users can effectively alleviate the energy supply pressure. In the upper-layer urban VPP optimal operation scheduling model, the scheduling plans for generating units and trading plans for the electricity wholesale market can be determined. Through upper-layer scheduling, it can not only meet the energy demand of users, but also minimize the urban VPP operation cost. The experimental results show that the proposed bi-layer optimization model for urban VPP operation considering IDR can achieve urban energy resources optimal allocation and support urban energy supply-demand balance, on the premise of ensuring economic benefits.

Effective demand response program addresing carbon constrained economic dispatch problem of a microgrid system

Electricity is produced, transferred, and delivered within a small geographic area using a constrained power distribution system called a microgrid. Such microgrids employed to guarantee that renewable energy sources (RES) are utilised to their fullest extent. Additional advantages of microgrids include reduced losses during transmission and related costs. The objectives of economic dispatch, emission dispatch, combined economic emission dispatch (CEED) founded on fractional programming (FP), and environmental constrained economic dispatch (ECED) are compared and contrasted in this study. The feasibility of a low-voltage microgrid system is examined for three situations. A novel, robust, and hybrid swarm-intelligence optimisation algorithm, based on merging the characteristics of the conventional grey-wolf optimizer (GWO), sine-cosine algorithm (SCA), and crow search algorithm (CSA), is used as a research optimisation tool. Results show that switching from a fixed pricing plan to a time-of-use (TOU) model resulted in a 15% less cost in production during the period of the research. When a portion of the microgrid customers engaged in the incentive-based demand response (IBDR) programme, further reductions in total generation costs and emissions were achieved. The peak demand was reduced by 3.5% as a result of participation in the IBDR programme, from 90 kW to 86.863 kW. and provided participants with incentives, according to numerical results.

Institutional and contextual drivers of and barriers to incentive-based demand response: A comparative case study in the Pacific Northwest

Despite demand response (DR) having great potential for improving the functioning of the electrical grid, it remains underutilized as a peak-load management strategy in the U.S. electricity sector. This research employs a comparative case study to examine institutional and contextual drivers of and barriers to incentive-based DR development by investor-owned utilities. The results show that state utility regulator rulings facilitated DR within the rate-of-return regulatory framework, which tends to incentivize investment in supply-side resources. This approach represents a case of institutional layering, in which new rules supplement the existing ones rather than replace them. The findings suggest that DR remains a highly technical policy area with limited public awareness. The results show that winter DR programs are a novel challenge, which has important implications as heating becomes increasingly electrified and the need for integrating renewables increases. The study discusses the importance of Information and Communication Technologies and automation in advanced forms of DR but draws attention to the risk of uneven distribution of benefits brought by these technologies. Finally, the study describes the synergistic effects of combining the benefits of energy efficiency and DR programs.

An improved decentralized scheme for incentive-based demand response from residential customers

Demand response is becoming increasingly important due to the high penetration of intermittent and variable renewable energy and electric vehicles in power systems. Real-time Incentive-Based Demand Response (IBDR) is helpful for power balancing in normal and emergency scenarios. The paper focuses on making an efficient IBDR scheme and increasing the participation of residential customers. A feature in the Home Energy Management System (HEMS) that provides the quantity of flexible load demand available with the residential customers for real-time IBDR in a decentralized scheme is explored. This paper analyzes the scheduling of flexible residential appliances in a dispersed manner over time by HEMS to increase the participation of residential customers in real-time IBDR. The error accumulation in the IBDR in a decentralized scheme due to the discrete character of residential appliances is examined and addressed. A financial ratio is proposed to give a fair opportunity to all the participants in IBDR when the cumulative flexible load demand available with the participants is greater than the required quantity of demand response. This paper provides algorithms that improve demand response systems by increasing the flexibility available to participants, reducing the error accumulation in IBDR, and ensuring the fairness of the IBDR opportunity to participants. The simulation analysis is done with 400 residential customers with a different number of flexible appliances and different energy requirements. The paper highlights the potential benefits of using a decentralized scheme to enable real-time IBDR in power systems.